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INDONESIA
EKSAKTA: Journal of Sciences and Data Analysis
Published by Universitas Islam Indonesia
ISSN : 27160459     EISSN : 27209326     DOI : 10.20885
Core Subject : Science, Engineering,
Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Materials Science & Nanotechnology
Ekstakta is an interdisciplinary journal with the scope of mathematics and natural sciences that is published by Fakultas MIPA Universitas Islam Indonesia. All submitted papers should describe original, innovatory research, and modelling research indicating their basic idea for potential applications. The Journal particularly welcomes submissions that focus on the progress in the field of mathematics, statistics, chemistry, physics, biology and pharmaceutical sciences.
Arjuna Subject : Kimia(semua kategori) - Kimia (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "VOLUME 4, ISSUE 1, February 2023" : 6 Documents clear
Microencapsulation of Kaffir (Citrus Hystrix DC) Essential Oil Using Chitosan and Maltodextrin Coatings With Freeze Drying Process As AntiCellulitis Syahrul Fahriza Sendi; Tiara Fatikha Sari; Rizqi Hibatullah; Dwiarso Rubiyanto
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 1, February 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss1.art1

Abstract

Comparasion of The M, MM and S Estimator in Robust Regression Analysis on Indonesian Literacy Index Data 2018 Ditya Anggraheni Rahayu; Ummu Fitrotin Nursholihah; Galang Suryaputra; Sugiyarto Surono
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 1, February 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss1.art2

Abstract

Regression analysis is a method used to determine the relationship between one dependent variable and one or more independent variables. However, the existence of outliers in the 2018 Community Literacy Development Index data led to the application of statistical methods not sensitive to pencils for analysis. This was the reason for adopting robust regression methods, including the M, S, and MM estimations. Therefore, this research aims to compare these three estimates and select the one with the best estimate based on the parameter estimation model associated with the RSE and R2 values. Descriptive and inferential analysis with robust regression was used due to several outlier data and to provide good regression model results with unbiased values. It was discovered that the S-estimator and MM-estimator are the best methods because they have the most minor Residual Standard Error (RSE) of 1.856 and R2 of 0.9778.
Synthesis of Carbon from Rice Groats at Various Pyrolysis Temperatures and Its Application for the Recovery of Chromium Wastewater from the Tannery Industry Wahyu Fajar Winata; Ika Yanti
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 1, February 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss1.art3

Abstract

The research on synthesis of carbon from rice groats at various pyrolysis temperatures and its application for the recovery of chromium wastewater from the tannery industry by varying pyrolysis temperatures at 300, 400, and 500 °C. The results of carbon synthesis are analyzed in the form of determination of yield, water content, ash content, and iodine number. The best pyrolysis condition with the quality requirement of carbon is at a pyrolysis temperature of 500 °C with 760 mg.g-1 (iodine number). Adsorption kinetics was also carried out to determine the adsorption kinetics of chromium ions from tannery wastewater. Adsorption kinetics model of chromium wastewater from the tannery industry corresponds to the pseudo-second-order kinetics model with R2 is 0.9857, k2 is 0.0093 g.mg-1.min-1, and qe is 28.5714 mg.g-1.
English English Setya Ayu Aprilia; Surjani Wonorahardjo; Yudhi Utomo
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 1, February 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss1.art6

Abstract

An analytical method was carried out to identify volatile compounds that play a role in the coffee aroma and their stability. Because it is unstable at high temperatures, the effect of the injection temperature on the GC/MS column on changes in compounds profile was observed. The aim of this research is also to develop analytical methods for coffee analysis using TPI-GC/MS method. Samples of Lemar Arabica coffee were taken from the Wonosantri Abadi plantation, Singosari, Malang, and roasted at 210°C. The roasted coffee was extracted using the soxhletation method and methanol as solvent. The compound profiles were analyzed using the GC/MS method with injection temperatures of 40°C, 140°C, and 240°C. The results showed that ketones, esters, furans, and thiazoles play a role in the aroma of coffee. The compounds present in roasted coffee injected at 40°C were less than those at injection temperatures of 140°C and 240°C based on the chromatograms. The profile of the compound at the injection temperature of 240°C is also more diverse than the others because the large injection temperature allows decomposition to occur so that there are many fractional compounds from the thermal decomposition. Toluene is the most stable compound because it appears at all three injection temperatures. Non-volatile caffeine compounds were also detected at an injection temperature of 240°C.
Implementation of Panel Data Regression in the Analysis of Factors Affecting Poverty Levels in Bengkulu Province in 2017-2020: Implementation of Panel Data Regression Aprilia Dewi Anggraeni Chairunnisa; Achmad Fauzan
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 1, February 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss1.art5

Abstract

Economic resilience is certainly an important target in every country or region. One of the main concerns in the economy of a country is poverty. This study aims to explore data with panel data regression that was formed and find factors that affect poverty in Bengkulu province from 2017 to 2020. The secondary data utilized were obtained from the Central Bureau of Statistics (BPS) of the province of Bengkulu. The independent variables used are Gross Regional Domestic Product (GRDP), Human Development Index (HDI), Life Expectancy (LE), and Average Years of Schooling (AYS), while the dependent variable is the percentage of poverty in the form of per region. The best panel data model obtained is the Fixed Effect Model (FEM) model with a cross-section. Based on the results obtained, the significant variable in this model is the GRDP variable. From the prediction results, the values ​​obtained from Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) respectively are 6.59% for MAPE, 5.48 for MSE, and 2.4 for RMSE indicating that panel data analysis is very very good in terms of predicting poverty in Bengkulu province
Nowcasting the Transportation and Accommodation Sectors Growth using the Google Trends Index Dhiar Niken Larasati; Nucke Widowati Kusumo Projo
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 4, ISSUE 1, February 2023
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol4.iss1.art4

Abstract

This research aims to assess the possibility of the daily and weekly Google Trends Index (GTI) to predict the quarterly GDP growth. The U-MIDAS approach is utilized because it allows using of daily and weekly basis data to forecast quarterly indicators without aggregating them onto a quarterly basis hence it does not eliminate useful information on the daily and weekly data. This research uses quarterly GDP for the transportation sector and the accommodation and restaurant sector which are considered potential industries for the future of Indonesia's economy. The result shows that the daily basis GTI can effectively predict the quarterly GDP growth better than the weekly basis GTI based on the RSE scores.

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